Fast and accurate influenza forecasting in the United States with Inferno

Infectious disease forecasting is an emerging field and has the potential to improve public health through anticipatory resource allocation, situational awareness, and mitigation planning. By way of exploring and operationalizing disease forecasting, the U.S. Centers for Disease Control and Prevention (CDC) has hosted FluSight since the 2013/14 flu season, an annual flu forecasting challenge. Since FluSight’s onset, forecasters have developed and improved forecasting models in an effort to provide more timely, reliable, and accurate information about the likely progression of the outbreak. While improving the predictive performance of these forecasting models is often the primary objective, it is also important for a forecasting model to run quickly, facilitating further model development and improvement while providing flexibility when deployed in a real-time setting. In this vein I introduce Inferno, a fast and accurate flu forecasting model inspired by Dante, the top performing model in the 2018/19 FluSight challenge. When pseudoprospectively compared to all models that participated in FluSight 2018/19, Inferno would have placed 2nd in the national and regional challenge as well as the state challenge, behind only Dante. Inferno, however, runs in minutes and is trivially parallelizable, while Dante takes hours to run, representing a significant operational improvement with minimal impact to performance. Forecasting challenges like FluSight should continue to monitor and evaluate how they can be modified and expanded to incentivize the development of forecasting models that benefit public health.Author summary: Infectious disease forecasting, if accurate, timely, and reliable, can assist decision makers with resource allocation planning in an attempt to curb the negative impacts of an outbreak. Forecasting challenges, like the U.S. Centers for Disease Control and Prevention’s flu forecasting challenge, FluSight, provide a space for teams to develop and operationalize real-time forecasting models that benefit public health, with weekly forecasts made at the state-level, Health and Human Services region-level, and the United States. The ultimate goal of these models is to produce accurate forecasts within the constraints of the forecasting challenge. Having a forecasting model that runs quickly is also important for future scalability, model development, and operational flexibility. In this paper, I present a fast and accurate flu forecasting model, Inferno. Through retrospective comparisons with FluSight-participating models, Inferno was shown to be a leading forecasting model in the field. Inferno, however, runs in minutes not hours, as other leading forecasting models do. This reduction in runtime constitutes an advancement in flu forecasting, positioning Inferno to scale to more granular geographic units, like counties or health care providers.